Andrew Disher



Document Type



The aim of this study was to use data provided by the Department of Public Health in the state of Massachusetts on its online dashboard to produce a time series model to accurately forecast the number of new confirmed deaths that have resulted from the spread of CoViD-19. Multiple different time series models were created, which can be classified as either an Auto-Regressive Integrated Moving Average (ARIMA) model or a Regression Model with ARIMA Errors. Two ARIMA models were created to provide a baseline forecasting performance for comparison with the Regression Model with ARIMA Errors, which used the number of CoViD-19 patients in hospitals as an exogenous variable to help make forecasts. These models were successfully constructed, passed all diagnostic tests and, after comparing the models’ one week forecasts with a variety of forecast error measures, the Regression Model with ARIMA Errors was found to be a superior method to forecast new confirmed deaths of CoViD-19 in Massachusetts.



Thesis Comittee

Dr. Wanchunzi Yu, Thesis Advisor

Dr. Kevin Rion, Committee Member

Dr. Laura K. Gross, Committee Member

Copyright and Permissions

Original document was submitted as an Honors Program requirement. Copyright is held by the author.